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            <ul>
<li><a class="reference internal" href="#">Visualizing cross-validation behavior in scikit-learn</a><ul>
<li><a class="reference internal" href="#visualize-our-data">Visualize our data</a></li>
<li><a class="reference internal" href="#define-a-function-to-visualize-cross-validation-behavior">Define a function to visualize cross-validation behavior</a></li>
<li><a class="reference internal" href="#visualize-cross-validation-indices-for-many-cv-objects">Visualize cross-validation indices for many CV objects</a></li>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-model-selection-plot-cv-indices-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
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<div class="sphx-glr-example-title section" id="visualizing-cross-validation-behavior-in-scikit-learn">
<span id="sphx-glr-auto-examples-model-selection-plot-cv-indices-py"></span><h1>Visualizing cross-validation behavior in scikit-learn<a class="headerlink" href="#visualizing-cross-validation-behavior-in-scikit-learn" title="Permalink to this headline">¶</a></h1>
<p>Choosing the right cross-validation object is a crucial part of fitting a
model properly. There are many ways to split data into training and test
sets in order to avoid model overfitting, to standardize the number of
groups in test sets, etc.</p>
<p>This example visualizes the behavior of several common scikit-learn objects
for comparison.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="p">(</span><span class="n">TimeSeriesSplit</span><span class="p">,</span> <span class="n">KFold</span><span class="p">,</span> <span class="n">ShuffleSplit</span><span class="p">,</span>
                                     <span class="n">StratifiedKFold</span><span class="p">,</span> <span class="n">GroupShuffleSplit</span><span class="p">,</span>
                                     <span class="n">GroupKFold</span><span class="p">,</span> <span class="n">StratifiedShuffleSplit</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Patch</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1338</span><span class="p">)</span>
<span class="n">cmap_data</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span>
<span class="n">cmap_cv</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span>
<span class="n">n_splits</span> <span class="o">=</span> <span class="mi">4</span>
</pre></div>
</div>
<div class="section" id="visualize-our-data">
<h2>Visualize our data<a class="headerlink" href="#visualize-our-data" title="Permalink to this headline">¶</a></h2>
<p>First, we must understand the structure of our data. It has 100 randomly
generated input datapoints, 3 classes split unevenly across datapoints,
and 10 “groups” split evenly across datapoints.</p>
<p>As we’ll see, some cross-validation objects do specific things with
labeled data, others behave differently with grouped data, and others
do not use this information.</p>
<p>To begin, we’ll visualize our data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generate the class/group data</span>
<span class="n">n_points</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

<span class="n">percentiles_classes</span> <span class="o">=</span> <span class="p">[</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="o">.</span><span class="mi">6</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([[</span><span class="n">ii</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">perc</span><span class="p">)</span>
               <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">perc</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">percentiles_classes</span><span class="p">)])</span>

<span class="c1"># Evenly spaced groups repeated once</span>
<span class="n">groups</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([[</span><span class="n">ii</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span> <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)])</span>


<span class="k">def</span> <span class="nf">visualize_groups</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
    <span class="c1"># Visualize dataset groups</span>
    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span>  <span class="p">[</span><span class="o">.</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span>
               <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span>  <span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span>
               <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">],</span>
           <span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Data</span><span class="se">\n</span><span class="s1">group&#39;</span><span class="p">,</span> <span class="s1">&#39;Data</span><span class="se">\n</span><span class="s1">class&#39;</span><span class="p">],</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Sample index&quot;</span><span class="p">)</span>


<span class="n">visualize_groups</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="s1">&#39;no groups&#39;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="define-a-function-to-visualize-cross-validation-behavior">
<h2>Define a function to visualize cross-validation behavior<a class="headerlink" href="#define-a-function-to-visualize-cross-validation-behavior" title="Permalink to this headline">¶</a></h2>
<p>We’ll define a function that lets us visualize the behavior of each
cross-validation object. We’ll perform 4 splits of the data. On each
split, we’ll visualize the indices chosen for the training set
(in blue) and the test set (in red).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">group</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Create a sample plot for indices of a cross-validation object.&quot;&quot;&quot;</span>

    <span class="c1"># Generate the training/testing visualizations for each CV split</span>
    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr</span><span class="p">,</span> <span class="n">tt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">group</span><span class="p">)):</span>
        <span class="c1"># Fill in indices with the training/test groups</span>
        <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
        <span class="n">indices</span><span class="p">[</span><span class="n">tt</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="n">indices</span><span class="p">[</span><span class="n">tr</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>

        <span class="c1"># Visualize the results</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">),</span>
                   <span class="n">c</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">,</span>
                   <span class="n">vmin</span><span class="o">=-.</span><span class="mi">2</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mf">1.2</span><span class="p">)</span>

    <span class="c1"># Plot the data classes and groups at the end</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">1.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span>
               <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>

    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">2.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span>
               <span class="n">c</span><span class="o">=</span><span class="n">group</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>

    <span class="c1"># Formatting</span>
    <span class="n">yticklabels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_splits</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">,</span> <span class="s1">&#39;group&#39;</span><span class="p">]</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">yticks</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_splits</span><span class="o">+</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">yticklabels</span><span class="p">,</span>
           <span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;Sample index&#39;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;CV iteration&quot;</span><span class="p">,</span>
           <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="n">n_splits</span><span class="o">+</span><span class="mf">2.2</span><span class="p">,</span> <span class="o">-.</span><span class="mi">2</span><span class="p">],</span> <span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">cv</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">ax</span>
</pre></div>
</div>
<p>Let’s see how it looks for the <a class="reference internal" href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>
cross-validation object:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">cv</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
</pre></div>
</div>
<p>As you can see, by default the KFold cross-validation iterator does not
take either datapoint class or group into consideration. We can change this
by using the <code class="docutils literal notranslate"><span class="pre">StratifiedKFold</span></code> like so.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">cv</span> <span class="o">=</span> <span class="n">StratifiedKFold</span><span class="p">(</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
</pre></div>
</div>
<p>In this case, the cross-validation retained the same ratio of classes across
each CV split. Next we’ll visualize this behavior for a number of CV
iterators.</p>
</div>
<div class="section" id="visualize-cross-validation-indices-for-many-cv-objects">
<h2>Visualize cross-validation indices for many CV objects<a class="headerlink" href="#visualize-cross-validation-indices-for-many-cv-objects" title="Permalink to this headline">¶</a></h2>
<p>Let’s visually compare the cross validation behavior for many
scikit-learn cross-validation objects. Below we will loop through several
common cross-validation objects, visualizing the behavior of each.</p>
<p>Note how some use the group/class information while others do not.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cvs</span> <span class="o">=</span> <span class="p">[</span><span class="n">KFold</span><span class="p">,</span> <span class="n">GroupKFold</span><span class="p">,</span> <span class="n">ShuffleSplit</span><span class="p">,</span> <span class="n">StratifiedKFold</span><span class="p">,</span>
       <span class="n">GroupShuffleSplit</span><span class="p">,</span> <span class="n">StratifiedShuffleSplit</span><span class="p">,</span> <span class="n">TimeSeriesSplit</span><span class="p">]</span>


<span class="k">for</span> <span class="n">cv</span> <span class="ow">in</span> <span class="n">cvs</span><span class="p">:</span>
    <span class="n">this_cv</span> <span class="o">=</span> <span class="n">cv</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="n">n_splits</span><span class="p">)</span>
    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
    <span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">this_cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>

    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="o">.</span><span class="mi">8</span><span class="p">)),</span> <span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="o">.</span><span class="mi">02</span><span class="p">))],</span>
              <span class="p">[</span><span class="s1">&#39;Testing set&#39;</span><span class="p">,</span> <span class="s1">&#39;Training set&#39;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">))</span>
    <span class="c1"># Make the legend fit</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">right</span><span class="o">=.</span><span class="mi">7</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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